Perceptual Biases in Font Size as a Data Encoding

Contributors

Eric Alexander, Chih-Ching Chang, Mariana Shimabukuro, Steven Franconeri, Christopher CollinsMichael Gleicher

Abstract

Many visualizations, including word clouds, cartographic labels, and word trees, encode data within the sizes of fonts. While font size can be an intuitive dimension for the viewer, using it as an encoding can introduce factors that may bias the perception of the underlying values. Viewers might conflate the size of a word’s font with a word’s length, the number of letters it contains, or with the larger or smaller heights of particular characters (‘o’ vs. ‘p’ vs. ‘b’). We present a collection of empirical studies showing that such factors-which are irrelevant to the encoded values-can indeed influence comparative judgements of font size, though less than conventional wisdom might suggest. We highlight the largest potential biases, and describe a strategy to mitigate them.

Publications

  • E. Alexander, C. Chang, M. Shimabukuro, S. Franconeri, C. Collins, and M. Gleicher, “The Biasing Effect of Word Length in Font Size Encodings,” Proc IEEE Information Visualization (InfoVis), Posters , 2016. Honorable Mention for Best Poster.
    [Bibtex] [PDF]
    @poster{shi2016,
    author = {Eric Alexander and Chih-Ching Chang and Mariana Shimabukuro and Steven Franconeri and Christopher Collins and Michael Gleicher},
    title = {The Biasing Effect of Word Length in Font Size Encodings},
    venue = { Proc IEEE Information Visualization (InfoVis), Posters },
    series = {Poster},
    note = {Honorable Mention for Best Poster},
    year = 2016
    }
  • E. Alexander, C. Chang, M. Shimabukuro, S. Franconeri, C. Collins, and M. Gleicher, “Perceptual Biases in Font Size as a Data Encoding,” , 2017. To appear.
    [Bibtex] [PDF] [DOI]
    @article{ale2017a,
      title={Perceptual Biases in Font Size as a Data Encoding},
      author={Eric Alexander and Chih-Ching Chang and Mariana Shimabukuro and Steven Franconeri and Christopher Collins and Michael Gleicher},
      publisher={TVCG Journal},
      year=2017,
      doi={10.1109/TVCG.2017.2723397},
      note={To appear}
    }

Video

Research

EduApps – Supporting Non-Native English Speakers to Overcome Language Transfer Effects

Metatation: Annotation as Implicit Interaction to Bridge Close and Distant Reading

DataTours: A Data Narratives Framework

Perceptual Biases in Font Size as a Data Encoding

Progressive Learning of Topic Modeling Parameters: A Visual Analytics Framework

Abbreviating Text Labels on Demand

NEREx: Named-Entity Relationship Exploration in Multi-Party Conversations

ConToVi: Multi-Party Conversation Exploration using Topic-Space Views

PhysioEx: Visual Analysis of Physiological Event Streams

Using Visual Analytics of Heart Rate Variation to Aid in Diagnostics

Off-Screen Desktop

PivotSlice

Reading Comprehension on Mobile Devices

#FluxFlow: Visual Analysis of Anomalous Information Spreading on Social Media

Optimizing Hierarchical Visualizations with the Minimum Description Length Principle

Lexichrome

SentimentState: Exploring Sentiment Analysis on Twitter

Facilitating Discourse Analysis with Interactive Visualization

DimpVis

Glidgets

TandemTable

Simple Multi-Touch Toolkit

Exploring Text Entities with Descriptive Non-photorealistic Rendering

Investigating the Semantic Patterns of Passwords

Bubble Sets: Revealing Set Relations with Isocontours over Existing Visualizations

Parallel Tag Clouds to Explore Faceted Text Corpora

VisLink: Revealing Relationships Amongst Visualizations

DocuBurst: Visualizing Document Content using Language Structure

Tabletop Text Entry Techniques

Lattice Uncertainty Visualization: Understanding Machine Translation and Speech Recognition

WordNet Visualization

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